In the field of machine vision, a common need is to identify and subsequently repeatably find a reference site within an image. The image is captured by the machine-vision system, where the image is a digital representation of an object within the field of view. The reference site is used to orient the machine to the object. For the reference site to be useful, it must be reliably found and is preferably unique within the field of view of the image.
Uses for reference sites are in the inspection field, where probes must be positioned for test, and in computer control of manufacturing processes, where sequences of operations must be precisely aligned. In the field of inspection of semiconductor devices, reference sites often are flats or notches on an edge of a wafer.
In such applications, the reference site is chosen typically by an operator. Then, the machine-vision system captures an image of the reference site, which is then stored as a template or a model. Thereafter during production, also known in the industry as run-time, the machine-vision system acquires an image of the object, and attempts to find a feature in the image that matches the template or model of the reference site. When the template or model is located, the machine-vision system performs further operations on the run-time images, such as positioning, searching for features or deviation analysis.
In printing applications, frequently reference sites are fiducial marks on the periphery of the object in view. For instance, crosses positioned at edges allow documents to be properly positioned for scanning. Typically, fiducials positioned at edges also orient automatic devices prior to printing.
Many newly designed applications, however, have removed fiducial marks from the objects being viewed. Increasingly unique characteristics from the natural patterns of the image are used as reference sites instead of the fiducial marks.
Without fidicials, operators must select the reference sites based on the "feel" of the image. To choose a viable reference site, which result in commercially good operation, the operator interacting with the selection program must be highly trained. Otherwise, the reference site is found inconsistently during production, if at all. Moreover, as the patterns within the images become more complex, the operator's job becomes even more difficult. Consequently, locating the reference site at run-time has become even less reliable.
In addition to unreliability introduced by the lack of operator training, the use of patterns in the image as the reference site has inherent difficulties. When patterns are used as the reference site, the reference site is a portion of the image that is segmented by a window. A unique pattern within a window that has minimal information in a particular direction may not be a suitable reference site for positioning in that direction. Such a reference site cannot indicate whether the image is upside down. Further, the pattern may not be unique within the field of view such as feature 140 and feature 140' illustrated in image 100 of FIG. 1. Without further processing, the machine cannot choose which of the features 140 or 140' is the reference site. Therefore, the machine may not be oriented properly with respect to the image. Thus, any non-unique feature is not an optimum reference site.
One attempt to replace operators with machines has had limited success owing to the inability to identify the appropriate criteria that repeatably represent a good reference site while producing a fast result. The attempt was embodied in AUTOTRAIN, a software tool sold by Cognex Corporation. AUTOTRAIN measured the standard deviation of two one-dimensional projections and uniqueness of windows to determine if a window was a good reference site candidate. Although AUTOTRAIN was fast, it did not always choose the appropriate reference site, and, at times, would propose a reference site of random noise.